Search Results - subject:"large dimensional asymptotics"

Quadratic shrinkage for large covariance matrices

Ledoit, Olivier; Wolf, Michael - 2019

) estimator in finite samples and recent progress under large-dimensional asymptotics. Our formula is quadratic: it has two … gravitation. We prove that no cubic or higher- order nonlinearities beat quadratic with respect to Frobenius loss under large-dimensional … asymptotics. Non-normality and the case where the matrix dimension exceeds the sample size are accommodated. Monte Carlo …

Persistent link: https://www.econbiz.de/10012140662

Shrinkage estimation of large covariance matrices : keep it simple, statistician?

Ledoit, Olivier; Wolf, Michael - 2019

estimators that can handle all regular functional transformations of the population covariance matrix under large-dimensional … asymptotics. We solve the problem of optimal covariance matrix estimation under a variety of loss functions motivated by …

Persistent link: https://www.econbiz.de/10012030045

Quadratic shrinkage for large covariance matrices

Ledoit, Olivier; Wolf, Michael - 2019

Persistent link: https://www.econbiz.de/10012123359

The power of (non-)linear shrinking : a review and guide to covariance matrix estimation

Ledoit, Olivier; Wolf, Michael - 2019

Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a difficult estimation problem; the sample covariance...

Persistent link: https://www.econbiz.de/10012018920

The power of (non-)linear shrinking : a review and guide to covariance matrix estimation

Ledoit, Olivier; Wolf, Michael - In: Journal of financial econometrics 20 (2022) 1, pp. 187-218

Persistent link: https://www.econbiz.de/10012878194

Optimal estimation of a large-dimensional covariance matrix under Stein's loss

Ledoit, Olivier; Wolf, Michael - 2017

within a class of nonlinear shrinkage estimators. The key is to employ large-dimensional asymptotics: the matrix dimension …

Persistent link: https://www.econbiz.de/10011663161

Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks

Ledoit, Olivier; Wolf, Michael - 2017

Markowitz (1952) portfolio selection requires an estimator of the covariance matrix of returns. To address this problem, we promote a nonlinear shrinkage estimator that is more flexible than previous linear shrinkage estimators and has just the right number of free parameters (that is, the...

Persistent link: https://www.econbiz.de/10011663163

Numerical implementation of the QuEST function

Ledoit, Olivier; Wolf, Michael - 2017

necessary to resort to an alternative framework known as large-dimensional asymptotics. Recently, Ledoit and Wolf (2015) have …-square criterion under large-dimensional asymptotics. It requires numerical inversion of a multivariate nonrandom function which they …

Persistent link: https://www.econbiz.de/10011663174

Direct nonlinear shrinkage estimation of large-dimensional covariance matrices

Ledoit, Olivier; Wolf, Michael - 2017

This paper introduces a nonlinear shrinkage estimator of the covariance matrix that does not require recovering the population eigenvalues first. We estimate the sample spectral density and its Hilbert transform directly by smoothing the sample eigenvalues with a variable-bandwidth kernel....

Persistent link: https://www.econbiz.de/10011784298

Central limit theorems for functionals of large sample covariance matrix and mean vector in matrix-variate location mixture of normal distributions

Bodnar, Taras; Mazur, Stepan; Parolya, Nestor - 2017

In this paper we consider the asymptotic distributions of functionals of the sample covariance matrix and the sample mean vector obtained under the assumption that the matrix of observations has a matrix-variate location mixture of normal distributions. The central limit theorem is derived for...

Persistent link: https://www.econbiz.de/10012654423